class_docstr: Balanced BCEWithLogitLoss based on https://github.com/NiFangBaAGe/Explicit-Visual-Prompt/blob/latest_branch/models/segformer.py
code: def forward(self, input: torch.Tensor, target: torch.Tensor):
      if input.dim() == 3:
          input = input.unsqueeze(1)
      eps = 1e-10
      count_pos = torch.sum(target) + eps
      count_neg = torch.sum(1.0 - target)
      ratio = count_neg / count_pos
      w_neg = count_pos / (count_pos + count_neg)
  
      bce1 = nn.BCEWithLogitsLoss(pos_weight=ratio)
      loss = w_neg * bce1(input, target)
  
      return loss
  
imports: import logging
  from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the BBCEWithLogitLoss obj's underlying member method code with example usage: obj.forward(input, target) and returns: loss